Abstract [en]

Objective: Nonlinear mixed-effect (NLME) modeling is the gold standard for the construction of population PK (PPK) models. However, naïve pooled data (NPD) methods may be more robust, are more invariant to model parameterization and often less time-consuming due to the structural simplicity of the method. These advantages may facilitate NLME model selection. This study aims to compare the performance between NLME and NPD in terms of PPK model selection based on NONMEM 7.3.

Method: NLME and NPD were compared for 13 previously developed PPK models which based on real data. Each developed model was structurally divided into five components, which contained an absorption delay model (oral administration model), absorption kinetic model, distribution model, elimination model and covariate model. For the 13 original models, 56 test models were generated by changing one of the components iteratively. The test models and original models were fitted to the corresponding real data using both NLME and NPD methods, followed by the calculation of the difference of objective function value (ΔOFV) between each test model and its related original model, for NLME and NPD separately (the OFV of test models minus original models). The best model was then selected for each comparison according to the theoretically expected 95% quantile of the χ2 distribution with degrees of freedom equal to the difference in the number of estimated parameters in component changes. In a second step, simulation studies were performed to test the sensitivity of the NLME and NPD methods to identifying different model structures. A ‘full’ PPK model with 2-compartment distribution kinetics, non-linear elimination and a transit-compartment first-order absorption model was used to simulate relatively densely sampled data. By varying parameters in the ‘full’ model, characteristics of the model could be emphasized or hidden. Relevant reduced models (one-compartment, linear elimination, no transit-compartment, zero order absorption) as well as the ‘full’ model were then fitted to the simulated data and the ability of the NPD and NLME methods to detect the true ‘full’ model were compared using ΔOFV, as in the real data examples.

Results: In the comparison of real data the model selection of the two methods was consistent for 48 out of 56 test models. 5 out of 8 exceptions in this comparison occurred when the test model and original model differed in the distribution model, and 3 out of 8 differed in the elimination model, which indicated that the difference might exist in the selection of the distribution model and elimination model between NLME and NPD. For the simulation study, NLME and NPD show the same model selection inclination, however, the ΔOFV of NPD were invariably lower than ΔOFV of NLME, which implied that NPD is less powerful in model selection compared to NLME.